In a quantum processor, the device design and external controls together contribute to the quality of the target quantum operations. As we continuously seek better alternative qubit platforms, we explore the increasingly large device and control design space. Thus, optimization becomes more and more challenging. In this work, we demonstrate that the figure of merit reflecting a design goal can be made differentiable with respect to the device and control parameters. In addition, we can compute the gradient of the design objective efficiently in a similar manner to the back-propagation algorithm and then utilize the gradient to optimize the device and the control parameters jointly and efficiently. Therefore, our work extends the scope of the quantum optimal control to device design and provides an efficient optimization method. We also demonstrate the viability of gradient-based joint optimization over the device and control parameters through a few examples based on the superconducting qubits.

在量子处理器中，设备设计和外部控制共同影响目标量子操作的质量。随着我们不断寻求更好的替代量子比特平台，我们探索了越来越大的设备和控制设计空间。因此，优化变得越来越具有挑战性。在这项工作中，我们证明了反映设计目标的品质因数可以根据设备和控制参数进行区分。此外，我们可以以类似于反向传播算法的方式有效地计算设计目标的梯度，然后利用梯度联合有效地优化设备和控制参数。因此，我们的工作将量子优化控制的范围扩展到了器件设计，并提供了一种有效的优化方法。